Robust/Optimal Temperature Profile Control of a High-Speed Aerospace Vehicle Using Neural Networks

An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A I-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

Thermolabile ribonucleases from antarctic psychrotrophic bacteria: detection of the enzyme in various bacteria and purification from Pseudomonas fluorescens

Thirteen terrestrial psychrotrophic bacteria from Antarctica were screened for the presence of a thermolabile ribonuclease (RNAase-HL). The enzyme was detected in three isolates of Pseudomonas fluorescens and one isolate of Pseudomonas syringae. It was purified from one P. Fluorescens isolate and the molecular mass of the enzyme as determined by SDS-PAGE was 16 kDa. RNAase-HL exhibited optimum activity around 40 degrees C at pH 7.4. It could hydrolyse Escherichia coli RNA and the synthetic substrates poly(A), poly(C), poly(U) and poly(A-U). Unlike the crude RNAase from mesophilic P. Fluorescens and pure bovine pancreatic RNAase A which were active even at 65 degrees C, RNAase-HL was totally and irreversibly inactivated at 65 degrees C.

Detection of nif genes in nitrogen-fixing bacterial isolate from tomato (Lycopersicon esculentum Mill cv Pusa ruby)

Nitrogen-fixing bacterial isolate from the intercellular spaces of tomato root cortical cells was studied for the location of nif genes on the chromosomal or plasmid DNA. The bacterial isolate showed two plasmids of approximate molecular sizes of 220 and 120 kb. Klebsiella pneumoniae nif HDK probe hybridized with the chromosomal DNA and not with the plasmid DNA thereby showing that nif genes are localised on the chromosomal DNA.

Leak Detection In Pressure Tubes Of A Pressurized Heavy-Water Reactor By Acoustic-Emission Technique

Leak detection in the fuel channels is one of the challenging problems during the in-service inspection (ISI) of Pressurised Heavy Water Reactors (PHWRs). In this paper, the use of an acoustic emission (AE) technique together with AE signal analysis is described, to detect a leak that was ncountered in one (or more) of the 306 fuel channels of the Madras Atomic Power Station (PHWR), Unit I. The paper describes the problems encountered during the ISI, the experimental methods adopted and the results obtained. Results obtained using acoustic emission signal analysis are compared with those obtained from other leak detection methods used in such cases.

Development of specific DNA probes and their usage in the detection of Plasmodium vivax infection in blood

The application of nucleic acid probes, in the detection of pathogenic micro-organisms, has become an integral part of diagnostic technologies. In this study, Plasmodium vivax-specific DNA probes have been identified by carrying out genomic subtractive hybridization. In this approach, the recombinant clones from a P. vivax genomic library are screened with radiolabelled human and P. falciparum DNA. The colonies which react with labelled P. falciparum and human DNA are eliminated and those which do not produce any autoradiographic signal have been subjected to further screening procedures. Three Fl vivax specific DNA probes have been obtained by these repeated screenings. Further analyses indicate that these probes are specific and sensitive enough to detect P. vivax infection in clinical blood samples when used in a non-radioactive DNA hybridization assay. (C) 1995 Academic Press Limited

Performance of the generalized delta rule in structural damage detection

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations.

Link failure detection for maintaining session continuity in packet data networks

A link failure in the path of a virtual circuit in a packet data network will lead to premature disconnection of the circuit by the end-points. A soft failure will result in degraded throughput over the virtual circuit. If these failures can be detected quickly and reliably, then appropriate rerouteing strategies can automatically reroute the virtual circuits that use the failed facility. In this paper, we develop a methodology for analysing and designing failure detection schemes for digital facilities. Based on errored second data, we develop a Markov model for the error and failure behaviour of a T1 trunk. The performance of a detection scheme is characterized by its false alarm probability and the detection delay. Using the Markov model, we analyse the performance of detection schemes that use physical layer or link layer information. The schemes basically rely upon detecting the occurrence of severely errored seconds (SESs). A failure is declared when a counter, that is driven by the occurrence of SESs, reaches a certain threshold.For hard failures, the design problem reduces to a proper choice;of the threshold at which failure is declared, and on the connection reattempt parameters of the virtual circuit end-point session recovery procedures. For soft failures, the performance of a detection scheme depends, in addition, on how long and how frequent the error bursts are in a given failure mode. We also propose and analyse a novel Level 2 detection scheme that relies only upon anomalies observable at Level 2, i.e. CRC failures and idle-fill flag errors. Our results suggest that Level 2 schemes that perform as well as Level 1 schemes are possible.

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data.

Distributed intrusion detection in the presence of correlated sensor readings: Signal-space and communication-complexity view-point

The problem of sensor-network-based distributed intrusion detection in the presence of clutter is considered. It is argued that sensing is best regarded as a local phenomenon in that only sensors in the immediate vicinity of an intruder are triggered. In such a setting, lack of knowledge of intruder location gives rise to correlated sensor readings. A signal-space view-point is introduced in which the noise-free sensor readings associated to intruder and clutter appear as surfaces f(s) and f(g) and the problem reduces to one of determining in distributed fashion, whether the current noisy sensor reading is best classified as intruder or clutter. Two approaches to distributed detection are pursued. In the first, a decision surface separating f(s) and f(g) is identified using Neyman-Pearson criteria. Thereafter, the individual sensor nodes interactively exchange bits to determine whether the sensor readings are on one side or the other of the decision surface. Bounds on the number of bits needed to be exchanged are derived, based on communication-complexity (CC) theory. A lower bound derived for the two-party average case CC of general functions is compared against the performance of a greedy algorithm. Extensions to the multi-party case is straightforward and is briefly discussed. The average case CC of the relevant greaterthan (CT) function is characterized within two bits. Under the second approach, each sensor node broadcasts a single bit arising from appropriate two-level quantization of its own sensor reading, keeping in mind the fusion rule to be subsequently applied at a local fusion center. The optimality of a threshold test as a quantization rule is proved under simplifying assumptions. Finally, results from a QualNet simulation of the algorithms are presented that include intruder tracking using a naive polynomial-regression algorithm. 2010 Elsevier B.V. All rights reserved.

Polyaniline modified electrodes for detection of dyes

Polyaniline (PANI) is one of the most extensively used conjugated polymers in the design of electrochemical sensors. In this study, we report electrochemical dye detection based on PANI for the adsorption of both anionic and cationic dyes from solution. The inherent property of PANI to adsorb dyes has been explored for the development of electrochemical detection of dye in solution. The PANI film was grown on electrode via electrochemical polymerization. The as grown PANI film could easily adsorb the dye in the electrolyte solution and form an insulating layer on the PANI coated electrode. As a result, the current intensity of the PANI film was significantly altered. Furthermore, PANI coated stainless steel (SS) electrodes show a change in the current intensity of Fe2+/Fe3+ redox peaks due to the addition of dye in electrolyte solution. PANI films coated on both Pt electrodes and non-expensive SS electrodes showed the concentration of dye adsorbed is directly proportional to the current intensity or potential shift and thus can be used for the quantitative detection of textile dyes at very low concentrations. (C) 2011 Elsevier B.V. All rights reserved.

Transitional intermittency detection by neural network

A neural network has been used to predict the flow intermittency from velocity signals in the transition zone in a boundary layer. Unlike many of the available intermittency detection methods requiring a proper threshold choice in order to distinguish between the turbulent and non-turbulent parts of a signal, a trained neural network does not involve any threshold decision. The intermittency prediction based on the neural network has been found to be very satisfactory.

Vibration confinement in a generic launch vehicle

A controller design for vibration control and alignment maintenance at critical location is developed in a generic launch vehicle whose equipment bay (EB) houses the main inertial platform. The controller uses active control to reduce the attitude disturbance in the attitude at the EB due to elastic deformation. The vibration energy is redistributed by the technique of vibration confinement, which enables the response at the EB to reach its steady state faster in the remaining portion of the structure. (AIAA)

A fault-tolerant distributed deadlock detection algorithm

In this paper, we propose a new fault-tolerant distributed deadlock detection algorithm which can handle loss of any resource release message. It is based on a token-based distributed mutual exclusion algorithm. We have evaluated and compared the performance of the proposed algorithm with two other algorithms which belong to two different classes, using simulation studies. The proposed algorithm is found to be efficient in terms of average number of messages per wait and average deadlock duration compared to the other two algorithms in all situations, and has comparable or better performance in terms of other parameters.

GNSS Signal Detection Under Noise Uncertainty

High sensitivity detection techniques are required for indoor navigation using Global Navigation Satellite System (GNSS) receivers, and typically, a combination of coherent and non- coherent integration is used as the test statistic for detection. The coherent integration exploits the deterministic part of the signal and is limited due to the residual frequency error, navigation data bits and user dynamics, which are not known apriori. So, non- coherent integration, which involves squaring of the coherent integration output, is used to improve the detection sensitivity. Due to this squaring, it is robust against the artifacts introduced due to data bits and/or frequency error. However, it is susceptible to uncertainty in the noise variance, and this can lead to fundamental sensitivity limits in detecting weak signals. In this work, the performance of the conventional non-coherent integration-based GNSS signal detection is studied in the presence of noise uncertainty. It is shown that the performance of the current state of the art GNSS receivers is close to the theoretical SNR limit for reliable detection at moderate levels of noise uncertainty. Alternate robust post-coherent detectors are also analyzed, and are shown to alleviate the noise uncertainty problem. Monte-Carlo simulations are used to confirm the theoretical predictions.

Improved large-MIMO detection based on damped belief propagation

In this paper, we consider the application of belief propagation (BP) to achieve near-optimal signal detection in large multiple-input multiple-output (MIMO) systems at low complexities. Large-MIMO architectures based on spatial multiplexing (V-BLAST) as well as non-orthogonal space-time block codes(STBC) from cyclic division algebra (CDA) are considered. We adopt graphical models based on Markov random fields (MRF) and factor graphs (FG). In the MRF based approach, we use pairwise compatibility functions although the graphical models of MIMO systems are fully/densely connected. In the FG approach, we employ a Gaussian approximation (GA) of the multi-antenna interference, which significantly reduces the complexity while achieving very good performance for large dimensions. We show that i) both MRF and FG based BP approaches exhibit large-system behavior, where increasingly closer to optimal performance is achieved with increasing number of dimensions, and ii) damping of messages/beliefs significantly improves the bit error performance.